Routed experts (ffn.experts.*): INT4 W4A16, group_size=32, symmetric, pack-quantized
Dense (attn.wq_*, wkv, wo_*, indexer.wq_b, shared_experts, main_proj): FP8 W8A16, e4m3 + fp32 block scales, 128×128 blocks, float-quantized
Embeddings, norms, gating, hc_*, indexer, lm_head: BF16 passthrough
KV cache: FP8, tensor-symmetric

Calibration (1 500 records):

HuggingFaceTB/smoltalk2 SFT/OpenThoughts3_1.2M_think — 336
Team-ACE/ToolACE train — 332
nvidia/Open-SWE-Traces openhands/qwen35_122b — 236
zai-org/LongCite-45k train — 232
caskcsg/NExtLong-128K-dataset train — 232
caskcsg/NExtLong-512K-dataset train — 132

Quality (per-expert rel-L2 vs FP): mean 1.47 %, w13 0.96 %, w2 1.68 %, dead experts 2 750 / 27 136 (10.1 %), worst layer 1 at 3.16 %, worst single expert layer-3/exp-207 w2 at 4.31 %.

Size: 165 GB, 46 safetensors shards.

Usage:

bash

vllm serve BlivionIaG/DeepSeek-V4-Flash-0731-Int4-FP8 \
  --trust-remote-code --kv-cache-dtype fp8 --block-size 256 \
  --tensor-parallel-size 4 --enable-expert-parallel \
  --moe-backend deep_gemm_mega_moe \
  --attention-config '{}' \
  --speculative-config '{"method":"dspark","num_speculative_tokens":7,"draft_sample_method":"greedy"}'

License: MIT — same as deepseek-ai/DeepSeek-V4-Flash-0731.

Cite:

@misc{deepseekai2026deepseekv4,
      title={DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence},
      author={DeepSeek-AI},
      year={2026},
}